Neural Collaborative Subspace Clustering

Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

10 Citations (Scopus)


We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and one based on a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.

Original languageEnglish
Title of host publicationProceedings of the 36th International Conference on Machine Learning
EditorsKamalika Chaudhuri, Ruslan Salakhutdinov
Place of PublicationStroudsburg PA USA
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Electronic)9781510886988
Publication statusPublished - 2019
EventInternational Conference on Machine Learning 2019 - Long Beach, United States of America
Duration: 9 Jun 201915 Jun 2019
Conference number: 36th (Website) (Proceedings)

Publication series

Name36th International Conference on Machine Learning, ICML 2019


ConferenceInternational Conference on Machine Learning 2019
Abbreviated titleICML 2019
Country/TerritoryUnited States of America
CityLong Beach
Internet address

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